Deep Convolution Neural Networks (Deep CNNs) are at the heart of the remarkable development in deep learning. CNNs have already been used in the 90's to solve problems of character recognition, but their use has become as widespread as it is now thanks to recent researches. These CNNs won the 2012 ImageNet image classification tournament, crushing other competitors. Then, the CNNs became a very useful tool in the field of machine learning.
Recently, the CNNs have been popular in an autonomous vehicles industry. When used in the autonomous vehicles industry, the CNNs perform functions of acquiring images from a camera installed on a vehicle, searching for lanes, etc. In order to perform these functions, the CNNs learn parameters by using training images on a real world and their corresponding GTs. Shortcoming of this kind of a traditional approach is that acquiring training images on a real world and generating their corresponding GTs should be done by people, not automatically by a programmed computer, resulting in high cost on training processes.
An alternative approach for training the CNNs is using virtual images on a virtual world simulated by a programmed computer. Herein, the virtual images and their corresponding GTs can be acquired automatically by the programmed computer, resulting in much lower cost on training processes, comparing to the traditional approach.
However, a shortcoming of this alternative approach is that the CNNs learn parameters by using the virtual images, resulting in degradation of a performance on detecting objects included in real images on the real world, whose characteristics are slightly different from those of the virtual images.